This is my first Tidy Tuesday contribution and will be playing around a little bit with the the Coffee rating data .
Libraries
tidyverse: data transformations and beautiful plots (Wickham et al. 2019).
hrbrthemes: this contains the ipsum theme, a very simple and elegant theme (Rudis 2020).
rmarkdown: all the structure of the report relies on this library (Allaire et al. 2020).
bookdown: allows the bibliography on the YAML header of this Rmarkdown doc (Xie 2016, 2020a).
epuRate: the elegant theme of the report (Holtz 2020).
knitr: all parts integration to render the output reporducible report (Xie 2014, 2015, 2020b).
citr: addin for easyly find citations in the .bib file and insert in the correct format (Aust 2019).
ggsci: a set of scientific palettes library and more… (Xiao 2018).
icon inserting many different icons in markdown (O’Hara-Wild 2020).
Getting Tidy Tuesday data
Downloading file 1 of 1: `coffee_ratings.csv`Coffee ratings distribution
p1 <- coffee %>%
drop_na(any_of("country_of_origin")) %>%
filter(aroma != 0) %>%
ggplot() +
aes(x = total_cup_points, fill = country_of_origin) +
geom_density() +
theme_minimal() +
scale_fill_viridis_d(alpha = 0.7) +
ylab("Proportion of coffes per country") +
xlab("Total cup points") +
theme(
plot.title = element_text(size = 18, face = "bold"),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.y = element_text(size = 16),
legend.text = element_text(size = 14),
legend.title = element_blank(),
legend.position = "bottom"
)
p2 <- coffee %>%
drop_na(any_of("country_of_origin")) %>%
filter(aroma != 0) %>%
ggplot() +
aes(x = total_cup_points, y = country_of_origin, fill = country_of_origin) +
geom_boxplot(show.legend = FALSE) +
theme_minimal() +
scale_fill_viridis_d(alpha = 0.7) +
ylab("Countries") +
xlab("Total cup points") +
labs(fill = "Country") +
theme(
plot.title = element_text(size = 18, face = "bold"),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
axis.text.y = element_text(size = 18)
) +
gghighlight(country_of_origin == "Colombia")
p2 / p1 + plot_annotation(tag_levels = 'A')
References
Allaire, JJ, Yihui Xie, Jonathan McPherson, Javier Luraschi, Kevin Ushey, Aron Atkins, Hadley Wickham, Joe Cheng, Winston Chang, and Richard Iannone. 2020. Rmarkdown: Dynamic Documents for R. https://CRAN.R-project.org/package=rmarkdown.
Aust, Frederik. 2019. Citr: RStudio Add-in to Insert Markdown Citations. https://CRAN.R-project.org/package=citr.
Holtz, Yan. 2020. EpuRate: A Clean Template for R Markdown Documents.
O’Hara-Wild, Mitchell. 2020. Icon: SVG Icons for R Documents and Apps. https://github.com/mitchelloharawild/icon.
Rudis, Bob. 2020. Hrbrthemes: Additional Themes, Theme Components and Utilities for ’Ggplot2’. https://CRAN.R-project.org/package=hrbrthemes.
Wickham, Hadley, Mara Averick, Jennifer Bryan, Winston Chang, Lucy D’Agostino McGowan, Romain Fran??ois, Garrett Grolemund, et al. 2019. “Welcome to the tidyverse.” Journal of Open Source Software 4 (43): 1686. https://doi.org/10.21105/joss.01686.
Xiao, Nan. 2018. Ggsci: Scientific Journal and Sci-Fi Themed Color Palettes for ’Ggplot2’. https://CRAN.R-project.org/package=ggsci.
Xie, Yihui. 2014. “Knitr: A Comprehensive Tool for Reproducible Research in R.” In Implementing Reproducible Computational Research, edited by Victoria Stodden, Friedrich Leisch, and Roger D. Peng. Chapman; Hall/CRC. http://www.crcpress.com/product/isbn/9781466561595.
———. 2015. Dynamic Documents with R and Knitr. 2nd ed. Boca Raton, Florida: Chapman; Hall/CRC. https://yihui.org/knitr/.
———. 2016. Bookdown: Authoring Books and Technical Documents with R Markdown. Boca Raton, Florida: Chapman; Hall/CRC. https://github.com/rstudio/bookdown.
———. 2020a. Bookdown: Authoring Books and Technical Documents with R Markdown. https://CRAN.R-project.org/package=bookdown.
———. 2020b. Knitr: A General-Purpose Package for Dynamic Report Generation in R. https://CRAN.R-project.org/package=knitr.
A work by Camilo Garcia